neural_network_chess VS boardlaw

Basically, MCTS is used in combination with reinforcement learning. This ensures that we get better evaluations for non terminal positions in training games. You might want to have a look at my free book @ https://github.com/asdfjkl/neural_network_chess

So consider instead keeping a running score of the position, rather than recalculating it in full every time. One of the main tricks behind Stockfish NNUE, the current top chess engine, is a clever way of not needing to fully recalculate the full neural net every time, but to do a delta update for each move for a substantial part of the network. I forget the main paper about that, but you can read about it in https://github.com/asdfjkl/neural_network_chess.

My book https://github.com/asdfjkl/neural_network_chess covers some of these topics...

Neural Networks For Chess

Shameless self-plug: I wrote a book about neural networks in chess and you can get a free PDF here https://github.com/asdfjkl/neural_network_chess (no ads, no fremium)

The 'probe envs' section further down gives one method for achieving this. Here's a concrete example from my recent work, where I'm building out a parallel MCTS (tricky!). There are three tests in the section I've highlighted, all testing the ability of the MCTS to estimate the value of a state in increasingly complex circumstances. All the tests decisively pass or fail because I sub'd out the env and agent for simple, deterministic variants. More, if - say - the trivial_test which uses a single player passes, but the test_two_player fails, that tells me the problem's something to do with how I'm handling multiple players.